Abstract- The generation of digital objects is a costly task and also there are very few tools that support the extraction of learning objects from existing digital materials. In this paper we present the general architecture of a system to extract learning objects from digital material, such as for example HTML pages or Word documents. One of the main features of the proposal is that objects are annotated with metadata generated according to the characteristics of instructional design.

Abstract- In recent years, Learning Content Management Systems have become an increasingly used option in the e-Learning for search and retrieval of specialized resources. Mainly because they provide environments where teachers catalog, publish and download resources in an easy way. The use of Learning Objects as storage units is a strong tendency in these systems. The Learning Objects promote reusability, and interoperability. This article presents a platform for Learning Object management; it uses assistance and recommendation approach for the Learning Objects development. AGORA offers an integrated set of tools for storage, search and reuse of instructional resources. Interoperability is guaranteed to be in accordance with specifications and standards defined in the e-Learning.

Abstract- This paper presents an ontology that integrates and provides knowledge related with Learning Objects design, with the intention of supporting the sequencing of learning objects. This ontology was built using an approach based on an ontological engineering methodology and data mining techniques for automatic acquisition of knowledge. The knowledge obtained is represented by the OWL ontology language and SWRL rule language. Finally we describe the role of ontology in a recommendation environment for developing learning resources.

Abstract- Complex Learning Processes (CLP) are the result of the dynamic and unanticipated integration of mixed pedagogies and resources, which facilitate learning in rich and personalized pedagogical environments. CLP consider factors such as the complexity and duration of the learning task, the degree of autonomy of the learner and the continuous design and control of the learning process. But their execution carries with implications such as dealing with long-lived learning activities and finishing activities without accomplishing their learning objectives. Advanced Transactional Models (ATM) relax some of the basic ACID transaction properties so applications may handle in a reliable way, several types of long-lived activities. In this paper, we propose a three-level transaction support implementation based on ATM for an EML execution engine focused on CLP, with the purpose of dealing with diverse types of learning activities, including long-lived activities and compensation activities for those performed activities unable to reach their learning objectives.